Performance Evaluation of SpliceAI for the Prediction of Splicing of NF1 Variants

Neurofibromatosis type 1, characterized by neurofibromas and cafe-au-lait macules, is one of the most common genetic disorders caused by pathogenic NF1 variants. Because of the high proportion of splicing mutations in NF1, identifying variants that alter splicing may be an essential issue for laboratories. Here, we investigated the sensitivity and specificity of SpliceAI, a recently introduced in silico splicing prediction algorithm in conjunction with other in silico tools. We evaluated 285 NF1 variants identified from 653 patients. The effect on variants on splicing alteration was confirmed by complementary DNA sequencing followed by genomic DNA sequencing. For in silico prediction of splicing effects, we used SpliceAI, MaxEntScan (MES), and Splice Site Finder-like (SSF). The sensitivity and specificity of SpliceAI were 94.5% and 94.3%, respectively, with a cut-off value of Delta Score > 0.22. The area under the curve of SpliceAI was 0.975 (p < 0.0001). Combined analysis of MES/SSF showed a sensitivity of 83.6% and specificity of 82.5%. The concordance rate between SpliceAI and MES/SSF was 84.2%. SpliceAI showed better performance for the prediction of splicing alteration for NF1 variants compared with MES/SSF. As a convenient web-based tool, SpliceAI may be helpful in clinical laboratories conducting DNA-based NF1 sequencing.

Automated summary

The study investigated the sensitivity and specificity of SpliceAI in predicting splicing alterations in NF1 variants, showing that SpliceAI outperformed MaxEntScan and Splice Site Finder-like in predictive performance. This novel in silico splicing prediction algorithm may be helpful for clinical laboratories conducting DNA-based NF1 sequencing.